Ethics Sheets for AI Tasks

Recent innovations such as Datasheets for Datasets and Model Cards for Model Reporting have made useful contributions to furthering ethical research. Yet, several high-profile events, such as the mass testing of emotion recognition systems on vulnerable sub-populations, have highlighted how technology will often lead to more adverse outcomes for those that are already marginalized. In this paper, I will make a case for thinking about ethical considerations not just at the level of individual models and datasets, but also at the level of AI tasks. I will present a new form of such an effort, Ethics Sheets for AI Tasks, dedicated to fleshing out the assumptions and ethical considerations hidden in how a task is commonly framed and in the choices we make regarding the data, method, and evaluation. Finally, I will provide an example ethics sheet for automatic emotion recognition. Ethics sheets are a mechanism to document ethical considerations \textit{before} building datasets and systems. Such pre-production activities (e.g., ethics analyses) and associated artifacts (e.g., accessible documentation) are crucial for responsible AI: for communicating risks to all stakeholders, to help decision and policy making, and for developing more effective post-production documents such as Data Sheets and Model Cards.